Automatic segmentation of cerebral hemispheres in magnetic resonance (MR) brain images help to quantify the brain asymmetry and correct several MR brain deformities. The detection of mid- sagittal plane (MSP) in human brain image is necessary to segment the hemispheres for both operator- based and automated brain image asymmetric analysis. In this paper a computationally simple and accurate technique to detect MSP in MRI human head scans using curve fitting is developed. The left and right hemispheres are segmented based on the detected MSP. The accuracy of the MSP is evaluated by comparing the segmented left and right hemispheres against the manually segmented ones. Experimental results using 78 volumes of T1, T2 and PD-weighted MRI brain images show that the proposed method has accurately segmented the cerebral hemispheres based on the detected MSP in axial and coronal orientations of normal and pathological brain images.
@article{Surya:MidSaggital-BrainMRI-IJIST-2015,
author = "P. Kalavathi and V. B. S. Prasath",
title = "Automatic segmentation of cerebral hemispheres in MR human head scans",
year = 2015,
journal = "International Journal of Imaging Systems and Technology - Neuroimaging and Brain Mapping",
month = "Dec",
keywords = "mid saggital, brain mri, segmentation, biomedical",
url = "http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291098-1098"
}
P. Kalavathi and V. B. S. Prasath. Automatic segmentation of cerebral hemispheres in MR human head scans. International Journal of Imaging Systems and Technology - Neuroimaging and Brain Mapping, December 2015.
Brain magnetic resonance images (MRI) plays a crucial role in neuroscience and medical diagnosis. Denoising brain MRI images is an important pre-processing step required in many of the automatic computed aided-diagnosis systems in neuroscience. Recently, nonlocal means (NLM) and variants of these filters, which are widely used in Gaussian noise removal from digital image processing, have been adapted to handle Rician noise which occur in MRI. One of the crucial ingredient for the successful image filtering with NLM is the patch similarity. In this work we consider the use of fuzzy Gaussian mixture model (FGMM) for determining the patch similarity in NLM instead of the usual Euclidean distance. Experimental results with different noise levels on synthetic and brain MRI images are given to highlight the advantage of the proposed approach. Comparison with other image filtering methods our scheme obtains better results in terms of peak signal to noise ratio and structure preservation.
@inproceedings{PrasathSIRS15_MRI,
author = "V. B. S. Prasath and P. Kalavathi",
title = "Adaptive nonlocal filtering for brain MRI restoration",
year = 2015,
booktitle = "Second International Workshop on Advances in Image Processing, Computer Vision, and Pattern Recognition (IWICP)",
publisher = "Springer SIST",
month = "Dec",
keywords = "restoration, brain mri, nonlocal filter, biomedical"
}
V. B. S. Prasath and P. Kalavathi. Adaptive nonlocal filtering for brain MRI restoration. Second International Workshop on Advances in Image Processing, Computer Vision, and Pattern Recognition (IWICP), Springer SIST, December 2015.
The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.
@article{Surya:Skullstrip-Review-JDI-2015,
author = "P. Kalavathi and V. B. S. Prasath",
title = "Methods on skull stripping of MRI head scan images - A review",
year = 2015,
journal = "Journal of Digital Imaging",
month = "Dec",
keywords = "skull stripping, brain mri, segmentation, review",
doi = "10.1007/s10278-015-9847-8"
}
P. Kalavathi and V. B. S. Prasath. Methods on skull stripping of MRI head scan images - A review. Journal of Digital Imaging, December 2015.